The Role of Machine Learning in Sports: Modelling and Predictions

Advancement in technology has availed various incredible innovations which are simplifying the humans’ work. The concept of artificial intelligence and machine learning like terms are turning into a reality. The fourth industrial revolution is combining the intelligence with a machine. Its real world example can be seen through predictive analytics, Sophia robot, recommendation engines, virtual assistance and many more inventions.

The machine learning powered predictive analytics model are used in various sectors such as sports, manufacturing, FinTech, retail, share market etc. In sports sector, it is getting used in various ways. You can take a live example of Germany winning FIFA last time. The management leverages the IBM analysis tool and SAP HANA to create effective strategies for game. It crunched and analyses the historical and trending data sets such as a players’ performance, winning, home ground, tactics and many more information gathered from thousands of data source. By analysing this data, team gained access to valuable insights to create their moves for winning whole match. The system was powerful enough to analyse the real-time collected data.

In soccer, you must have heard of term expected goals (xG). It is the total number of goals a team or a particular player has chances to have in the game. Football modelling and expected goalsare interconnected. The xG leverages the same predictive model which processes the huge data set to deliver the most accurate result.

With the help of statistics, graphs, tables and various more informative sources gained after performing operations like classification, analysis etc., one can predict the output of whole scenario.

It is also getting used in commentaries. Have you ever heard a commentator saying sentences like “He should have hat-trick of sixes” or “he shouldn’t give away this shot”. By using the past performance they can predict the possibility of winning or losing.

In soccer, goal doesn’t happen too frequently. It is estimated that goal is just limited to 2.5 goals per match. So, it is very challenging to make prediction for the game outcome. All the played shots are not same. Here, expected goals can help. It makes use of multiple figures from past data to predict who is more close to winning?

It is dependent over many scenarios such as team’s involvement in past matches, performance, loose ends, home ground performance, player’s actions, defence techniques etc.

No predictive model is 100 percent accurate. There can be anomalies in multiple factors. Such as a good player can miss or a poor player can perform fantastic in a match. More the accurate data used in modelling and analysis, the results will be more accurate.

The role of machine learning is not just limited to modelling. But also, it is getting used to decide the whole tournament strategy, rules and various more tasks. With advancement in empowering resources, its potential for solving human problems is also getting strong. There are models powerful enough to get you every single detail in just a few minutes. The whole sport world is aware of its incredible powers. A few organizations are using it to check their player’s mental performance and preparing them for tournament.